Go language development of door-to-door cooking system: How to implement menu recommendation function?

Go language development of door-to-door cooking system: How to implement menu recommendation function?

With the fast pace of life and busy work pressure, many people do not have the time and energy to cook their own meals. Therefore, home cooking services are becoming more and more popular. In order to provide a better user experience, our door-to-door cooking system needs to implement a menu recommendation function to meet the personalized needs of users.

Developing the menu recommendation function in Go language can be achieved with the help of machine learning and recommendation algorithms. Below we will introduce in detail the steps to implement the menu recommendation function and provide corresponding code examples.

Step One: Data Collection and Cleaning
To implement the menu recommendation function, we first need to collect and clean relevant data. Dish information, such as dish name, required ingredients, cooking steps, etc., can be obtained from recipe websites or other reliable data sources.

In the Go language, you can use the Go crawler library to crawl data from recipe websites. The following is a simple sample code:

package main

import (
    "fmt"
    "net/http"
    "io/ioutil"
)

func main() {
    url := "https://www.example.com/recipes"
    
    resp, err := http.Get(url)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    defer resp.Body.Close()
    
    body, err := ioutil.ReadAll(resp.Body)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    fmt.Println(string(body))
}

Step 2: Build user personalization model
In order to personalize the recommendation menu, we need to build the user's personalization model. This can be achieved by collecting user preferences and ordering history.

In the Go language, we can use ORM frameworks such as GORM or Xorm to operate the database and store users' personal information and ordering records. The following is a sample code:

package main

import (
    "fmt"
    "github.com/jinzhu/gorm"
    _ "github.com/jinzhu/gorm/dialects/mysql"
)

type User struct {
    gorm.Model
    Name    string
    Age     int
    Orders  []Order
}

type Order struct {
    gorm.Model
    UserID    uint
    MenuName  string
}

func main() {
    db, err := gorm.Open("mysql", "user:password@/dbname?charset=utf8&parseTime=True&loc=Local")
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    defer db.Close()

    // 创建表
    db.AutoMigrate(&User{})
    db.AutoMigrate(&Order{})

    // 存储用户信息和订餐记录
    user := User{Name: "Tom", Age: 25}
    order1 := Order{UserID: user.ID, MenuName: "宫保鸡丁"}
    order2 := Order{UserID: user.ID, MenuName: "鱼香肉丝"}

    db.Create(&user)
    db.Create(&order1)
    db.Create(&order2)
    
    // 查询用户的订餐记录
    var orders []Order
    db.Model(&user).Related(&orders)

    fmt.Println(user)
    fmt.Println(orders)
}

Step 3: Implement the recommendation algorithm
With the user’s personalized model and dish data, next we need to implement the recommendation algorithm. Commonly used recommendation algorithms include collaborative filtering and content-based recommendations.

In the Go language, you can use the corresponding library to implement the recommendation algorithm, such as go-recsys or go-learn. The following is a sample code using the collaborative filtering algorithm:

package main

import (
    "fmt"
    "github.com/sjwhitworth/golearn/base"
    "github.com/sjwhitworth/golearn/evaluation"
    "github.com/sjwhitworth/golearn/filters"
    "github.com/sjwhitworth/golearn/trees"
)

func main() {
    // 构建数据集
    rawData, err := base.ParseCSVToInstances("menu.csv", false)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    // 使用推荐算法对数据集进行训练和评估
    trainData, testData := base.InstancesTrainTestSplit(rawData, 0.7)
    
    tree := trees.NewID3DecisionTree(0.6)

    // 使用协同过滤算法进行训练
    filter := filters.NewChiMergeFilter(trainData, 0.999)
    trainDataFiltered := base.NewLazilyFilteredInstances(trainData, filter)
    
    tree.Fit(trainDataFiltered)
    
    // 对测试数据进行预测
    predictions, err := tree.Predict(testData)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    // 计算准确率
    confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
    if err != nil {
        fmt.Println("Error: ", err)
        return
    }
    
    accuracy := evaluation.GetAccuracy(confusionMat)
    fmt.Println("Accuracy: ", accuracy)
}

Through the above steps, we have completed the menu recommendation function developed in Go language. You can obtain dish data through the crawler library, use the ORM framework to store user personalized models and dish data, and use recommendation algorithms to make menu recommendations.

I hope this article will help you understand the Go language development of the door-to-door cooking system and implement the menu recommendation function. If you have any questions, please feel free to leave a message to communicate.

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